1,np.random.seed() 设置seed()里的数字就相当于设置了一个盛有随机数的“聚宝盆”,一个数字代表一个“聚宝盆”。 当在seed()的括号里设置相同的seed,“聚宝盆”就是一样的,当然每次拿出的随机数就会相同。 如果不设置seed,则每次会生成不同的随机数,但是有时候明明设置了seed()没有变,生成的随机数组还是不同
np.random.seed(args.seed) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed)log = None # Save model and meta-data. Always saves in a new folder. if args.save_folder: exp_counter = 0 save_folder = '{}/exp{}/'.format(args.save_folder, exp_counter)...
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() torch.manual_seed(args.seed) vae = VAE(args.z_dim) optimizer = Adam(vae.parameters(), lr=args.lr) device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') vae.to(device) # download mnist & setup loaders if args.mode == "train": train_set = ...